Large language models (LLMs) have demonstrated their limitations in addressing the design of active proteins that rely on intricate intramolecular interactions, particularly in the engineering of biocatalysts. Conducting real-world studies from targeted laboratory assays has become the de facto standard for artificial intelligence (AI) research in complex biological tasks. In this study, we present a standardized strategy using function-targeted models to decode the subtle effect of sequence var